Epilepsy affects over 60 million individuals worldwide, with one quarter of patients having disease refractory to standard therapies including medication and surgery. Automated seizure prediction algorithms have been studied for decades to improve the diagnosis and treatment of epilepsy. More recently, these algorithms have been applied to closed-loop implantable devices designed to detect pre-seizure events and electrically stimulate the brain to abort epileptic activity.
Conventional systems use real-time iEEG data as input to an algorithm to predict onset of epileptic activity and trigger targeted electrical stimulation to arrest potential seizures. However, these conventional systems are often limited by the efficacy of the prediction algorithms. The algorithms used in these conventional devices are typically dependent on extracting and analyzing specific “features” of the iEEG signal, such as amplitude, line length, and area under the curve. These conventional systems have been hampered by high false positive rates, causing unnecessary stimulation to the brain and increased frequency of repeat surgery to replace spent batteries.